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MCP Prompt Optimizer

by Bubobot-Team
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Python
Remote

MCP Prompt Optimizer

Python 3.8+ License: MIT MCP Compatible

A professional-grade MCP (Model Context Protocol) server that provides cutting-edge prompt optimization tools with research-backed strategies delivering 15-74% performance improvements.

✨ Features

🎯 Basic Optimization Strategies

  • Clarity: Simplifies prompts for directness and precision

  • Specificity: Adds detailed constraints and requirements

  • Chain of Thought: Incorporates step-by-step reasoning

  • Few-Shot: Includes example formats for guidance

  • Structured Output: Defines clear output organization

  • Role-Based: Adds expert role context

🚀 Advanced Optimization Strategies

  • Tree of Thoughts (ToT): Multi-path reasoning with 74% success rate on complex tasks

  • Constitutional AI: Self-critique and alignment with safety principles

  • Automatic Prompt Engineer (APE): AI-discovered optimal instruction patterns

  • Meta-Prompting: AI generates its own optimized prompts

  • Self-Refine: Iterative improvement with 20% performance gains

  • TEXTGRAD: Natural language feedback as optimization gradients

  • Medprompt: Multi-technique ensemble achieving 90%+ accuracy

  • PromptWizard: Feedback-driven self-evolving prompts

📋 Professional Domain Templates

Production-ready templates across 11 domains:

  • Business Analysis: Competitive analysis frameworks

  • Product Management: User research synthesis

  • Content Creation: Technical blog posts with SEO optimization

  • Development: Comprehensive code review checklists

  • Communication: Stakeholder updates and project reports

  • Strategy: OKR planning frameworks

  • Operations: Standard Operating Procedures (SOPs)

  • Legal: Contract termination and compliance

  • Customer Experience: Feedback surveys and insights

  • Data Analysis: Data insights and reporting

  • Meeting Management: Effective meeting agendas

🛠️ Installation

Quick Setup

# Clone the repository git clone <repository-url> cd mcp-prompt-optimizer # Create virtual environment (recommended) python3 -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate # Install dependencies ./install.sh # Or install manually pip install -r requirements.txt # Configure Claude Desktop python3 setup_interactive.py

Manual Configuration

Add to your Claude Desktop configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

Windows: %APPDATA%\Claude\claude_desktop_config.json

Linux: ~/.config/Claude/claude_desktop_config.json

{ "mcpServers": { "prompt-optimizer": { "command": "python3", "args": ["/path/to/mcp-prompt-optimizer/prompt_optimizer.py"], "env": {} } } }

🎮 Usage

Basic Commands

# Analyze prompt quality "Analyze this prompt: write a blog post about AI" # Apply specific optimization "Optimize this prompt using chain_of_thought: explain machine learning" # Auto-select best strategy "Auto-optimize: help me debug this code" # Get domain template "Get domain template for code_review_checklist"

Advanced Commands

# Use Tree of Thoughts for complex problems "Apply advanced optimization with tree_of_thoughts: design a microservices architecture" # Use Constitutional AI for safety-critical tasks "Apply advanced optimization with constitutional_ai: create content moderation guidelines" # Use Medprompt for high-accuracy classification "Apply advanced optimization with medprompt: categorize customer support tickets" # List available templates "List all domain templates"

🏗️ Architecture

mcp-prompt-optimizer/ ├── prompt_optimizer.py # Main MCP server ├── advanced_strategies.py # Research-backed optimization strategies ├── domain_templates.py # Professional domain templates ├── examples.py # Usage examples and demonstrations ├── setup_interactive.py # Automated setup script └── README.md # This file

🧪 Testing

# Run basic tests ./test.sh # Run usage examples python3 examples.py

📊 Performance Benchmarks

Strategy

Use Case

Performance Improvement

Tree of Thoughts

Complex reasoning

70-74% success rate

Medprompt

Classification tasks

90%+ accuracy

Self-Refine

Iterative improvement

20% per iteration

Constitutional AI

Safety alignment

High compliance

Chain of Thought

Step-by-step tasks

15-25% improvement

🔧 Available Tools

Core Tools

  1. analyze_prompt: Analyzes prompt quality and identifies issues

  2. optimize_prompt: Applies specific optimization strategies

  3. auto_optimize: Automatically selects optimal strategy

  4. get_prompt_template: Returns basic templates

Advanced Tools

  1. advanced_optimize: Applies research-backed strategies

  2. get_domain_template: Returns professional domain templates

  3. list_domain_templates: Lists available templates by domain

🎯 Strategy Selection Guide

Prompt Type

Recommended Strategy

Complex problems

tree_of_thoughts

Classification tasks

medprompt

Safety-critical

constitutional_ai

Vague requirements

meta_prompting

Needs refinement

self_refine

General optimization

auto

🤝 Contributing

We welcome contributions! Please:

  1. Fork the repository

  2. Create a feature branch

  3. Add tests for new functionality

  4. Update documentation

  5. Submit a pull request

Adding New Features

  • New Strategy: Add to advanced_strategies.py

  • New Template: Add to domain_templates.py

  • Examples: Add to examples.py

🐛 Troubleshooting

Common Issues

MCP not working?

  • Check Python version: python3 --version (requires 3.8+)

  • Install dependencies: Run ./install.sh or pip install -r requirements.txt

  • Verify MCP installation: pip show mcp

  • Check Claude Desktop logs

  • Restart Claude Desktop

Commands not recognized?

  • Verify configuration file location

  • Check file paths in configuration

  • Run setup script again

Debug Mode

# Test server directly python3 prompt_optimizer.py # Verbose logging export MCP_LOG_LEVEL=debug python3 prompt_optimizer.py

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

  • Research from Princeton, Google DeepMind, Microsoft Research

  • Anthropic's Constitutional AI framework

  • Stanford's DSPy framework

  • OpenAI's prompt engineering guidelines

📈 Citation

If you use this tool in your research or projects, please cite:

@software{mcp_prompt_optimizer, title={MCP Prompt Optimizer: Research-Backed Prompt Optimization for AI Systems}, author={Bubobot}, year={2024}, url={https://github.com/Bubobot-Team/mcp-prompt-optimizer} }

Built with ❤️ for the AI community

For questions, issues, or contributions, please visit our GitHub repository.

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